Presentation of Data Analysis and Interpretation
Presentation of Data Analysis and Interpretation
Objectives
At the end of the topic, you will be able to:
Understand the difference between presentation, analysis, and interpretation of data gathered.
Effectively present gathered data through graphs, tables, and figures.
Analyze and interpret the data gathered.
Processes Involved
Presentation of Data
Analysis of Data
Interpretation of Data
1. Presentation of Data
Definition: Presentation of data refers to the organization of data that is typically presented in charts, tables, or figures alongside textual interpretation.
Ways of Presenting Data
Textual
Tabular
Graphical
1. Textual
Characteristics:
Data is presented in paragraph form.
The presentation includes both text and numbers.
Example:
“Of the 150 samples interviewed, the following complaints are noted: 27 for lack of books in the library, 25 for a dirty playground, 20 for lack of laboratory equipment, and 17 for not maintained school buildings.”
2. Tabular
Description:
Provides exact values and illustrates results efficiently.
Enables the researcher to present a large amount of data in a compact space.
Elements of a Table
a. Title:
Example: "Frequency distribution of injury type at a workplace"
b. Rows:
Example: Injury Type
c. Label:
Example: Frequency, Percent
d. Columns:
Example:
Fall | 14 | 30%
Cut | 8 | 17%
Burn | 3 | 6%
Back injury | 2 | 4%
Other trauma | 11 | 23%
Injury not specified | 9 | 19%
e. Data:
Total: 47 | 100%
3. Graphical
Description:
Shows relations, comparisons, and distributions in a dataset using visuals.
Depicts absolute values, percentages, or index numbers using symbols (e.g., bars, lines, slices, or pictures).
Types of Graphs
Bar Graph: Shows the number of children who chose specific fruits (e.g., watermelon, orange, apple, banana).
Linear Graph: Displays trends over time.
Pie Graph: Illustrates proportions within a whole.
Ratio Chart and Statistical Map: Represent data relationships visually.
Pictogram: Uses pictures to convey information.
Reminder
Follow and use the sequence of the Standard Operating Procedures (SOP) in presenting data through tables or graphs.
2. Analysis of Data
Definition: Data analysis is the process of inspecting, rearranging, modifying, and transforming data to extract useful information.
Data Analyst
It is crucial for a data analyst to procure accurate and appropriate analysis.
A data analyst should possess the skills to analyze the statistics of data and turn it into insights.
Reminder
Maintain the integrity of credible data analysis.
3. Interpretation of Data
Definition: Interpretation of data refers to the implementation of processes through which data is reviewed to arrive at informed conclusions.
This process requires the intelligence and logic of the researcher, which serves as the basis for the findings of the study.
Example: Frequency and Percentage Distribution of Respondents based on Sex
Table Content:
SEX
FREQUENCY
PERCENTAGE
MALE
10
26.3%
FEMALE
28
73.7%
TOTAL
38
100%
Analysis:
10 or 26.3% of the respondents are male while 28 or 73.7% are female.
Related studies show that female respondents are generally dominant compared to males.
This data contrasts with the National Statistics Office report indicating a population of 92,337,852 in the Philippines with a slight male majority (50.4%).
Types of Data Analysis
Quantitative Data Analysis
Qualitative Data Analysis
Quantitative Data Analysis
Definition: A process of analyzing number-based data or data that can be easily converted into numbers without losing its meaning.
Processes: Simple math or more advanced statistical analyses are used to discover commonalities or patterns in data, with results often reported in graphs or tables.
Methods in Quantitative Data Analysis
Descriptive Statistics
Inferential Statistics
Descriptive Statistics
Definition: Describes a dataset to help understand the details or characteristics of the sample.
Includes:
Frequencies or counts
Percentages
Measures of central tendency: mean, median, mode
Measures of variability that indicate the spread or variation of responses.
Inferential Statistics
Definition: Used to predict characteristics about a larger population.
Applications:
Test hypotheses
Examine differences and correlations between groups
Includes tests: t-Tests, ANOVA, Chi-Square, and regression analysis to understand cause-and-effect relationships in data.
Qualitative Data Analysis
Definition: The systematic searching and arranging of interview transcripts, observation notes, audio/video recordings, or other non-textual materials to increase understanding of a phenomenon.
Processes:
Coding or categorizing data
Identifying significant patterns
Drawing meaningful conclusions and building a logical chain of evidence.
Coding
Definition: The process of labeling and organizing qualitative data to identify different themes and relationships.
Methodology:
Involves subdividing large volumes of raw information into categories.
Often done manually using colored pens for categorization, then cutting and sorting data accordingly.
Methods in Qualitative Data Analysis
Thematic Analysis
Content Analysis
Narrative Analysis
Discourse Analysis
Grounded Theory
Thematic Analysis
Description: Themes are extracted from text by analyzing word or sentence structure, leading to the identification of themes such as participation.
Content Analysis
Definition: Examines word frequency, patterns, and sequences of occurrence.
Narrative Analysis
Definition: Analyzes people's stories to organize and make sense of their accounts, ensuring they are functional and purposeful.
Discourse Analysis
Definition: Analyzes language and expression in various social contexts.
Grounded Theory
Definition: The theory developed is “grounded” in actual data, meaning theory analysis and development occurs post data collection.
Conclusion
A comprehensive understanding of the processes involved in presentation, analysis, and interpretation of data is essential for effective research practices.